An Autonomy Software Testbed Simulation for Ocean Worlds Missions

We present a new approach for synthesising Paretooptimal Markov decision process (MDP) policies that satisfy complex combinations of quality-of-service (QoS) software requirements. These policies correspond to optimal designs or configurations of software systems, and are obtained by translating MDP models of these systems into parametric Markov chains, and using multi-objective genetic algorithms to synthesise Pareto-optimal parameter values that define the required MDP policies. We use case studies from the service-based systems and robotic control software domains to show that our MDP policy synthesis approach can handle a wide range of QoS requirement combinations unsupported by current probabilistic model checkers. Moreover, for requirement combinations supported by these model checkers, our approach generates better Pareto-optimal policy sets according to established quality metrics.

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An Autonomy Software Testbed Simulation for Ocean Worlds Missions

Cited by 3 publications

References 4 publications

Towards Robust, Resilient Ocean World Science Sampling Systems

Towards Robust, Resilient Ocean World Science Sampling Systems

Icy Moon Surface Simulation and Stereo Depth Estimation for Sampling Autonomy

Evolutionary-Guided Synthesis of Verified Pareto-Optimal MDP Policies

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